The independent additive errors linear model consists of a structure for the mean and a separate structure for the error distribution. The error structure may be parametric or it may be semiparametric. Under alternative values of the mean structure, the best fitting additive errors model has an error distribution which can be represented as the convolution of the actual error distribution and t...

We study a new approach to statistical prediction in the DempsterShafer framework. Given a parametric model, the random variable to be predicted is expressed as a function of the parameter and a pivotal random variable. A consonant belief function in the parameter space is constructed from the likelihood function, and combined with the pivotal distribution to yield a predictive belief function ...

The Bayesian approach to analyzing semi-parametric models are gaining popularity in practice. For the Cox proportional hazard model, it has been shown recently that the posterior is consistent and leads to asymptotically accurate confidence intervals under a Lévy process prior on the cumulative hazard rate. The explicit expression of the posterior distribution together with independent incremen...

Bridge condition assessment by use of structural health monitoring (SHM) data has been recognized as a promising approach towards the condition-based preventive maintenance. In-service bridges are normally subjected to multiple types loads such highway traffic, railway wind and thermal effect, resulting in heterogeneous multimodal structure strain/stress responses. This study aims develop an SH...

Approximate dynamic programming (ADP) is a general methodological framework for multistage stochastic optimization problems in transportation, finance, energy, and other applications where scarce resources must be allocated optimally. We propose a new approach to the exploration/exploitation dilemma in ADP. First, we show how a Bayesian belief structure can be used to express uncertainty about ...